The present invention relates to the application of machine learning algorithms to skill-based entertainment machines, in particular to claw-style toy dispensing systems, hereby referred to as crane machines. Crane machines provide entertainment to players, chiefly through their ability to dispense toys under a combination of operator skill and machine capability. The general layout of such a crane machine is a collection of prizes to be dispensed which can be physically picked up by a claw or grabber attached to some style of multi-axis gantry. This multi-axis gantry is under direct player control, as is the grabber which can select the prize.
Most crane machines set the grabber pickup strength to change how difficult it is to grab the prizes, and therefore provide some degree of control over how many prizes the machine dispenses. All current crane machines employ a fixed setting or lookup table based approach to determine just how much force is applied to the grabber, based on time or number of toys dispensed. This provides a limited experience to the player, as well as providing difficulty in configuring the machines for different prize weights and sizes.
The present invention covers the means and methods to apply machine-based learning for a truly interactive crane game prize dispensing system, whereby the machine varies its grabber strength through a machine learning algorithm which operates internally on prize machine data, while outputting intelligently controlled and continuously variable power output to the grabber mechanism. In addition, the system described herein allows machine operators to quickly and automatically teach the machine new prize types, without needing to experience with settings and confusion. The combined effect is a much more enjoyable crane machine experience, with easier setup required by the operators.
The prior art in such crane machine technology includes several patents which go to great lengths to describe new technology applied to make crane machines smarter. Watanabe, in US20060170164A1, goes to great length to describe an RFID based means to alleviate the burden of the difficulty of adjusting the machine, which includes databases and readers and further technology. The embodiment discussed here also alleviates such burdens, but with data that already exists on most prize dispensing machines. Peck, in US20090191931A1, discloses several embodiments of a crane machine that includes options for interactive video, and presents “prizes, such as bonus time, that affect the subsequent attempt to obtain a prize”. No mention is made of machine learning or the systems used to affect the subsequent attempts to obtain prizes.
The closest patent to what is described here is Stubben, U.S. Pat. No. 6,283,475 B1 which covers apparatus and method for crane game claw control. The closest claim is #36, which presents “A method of controlling a solenoid which controls gripping strength of a claw in a crane game machine comprising: selecting a desired gripping strength for the claw; creating an electrical signal representative of the desired gripping strength; and delivering and maintaining a current to the solenoid based on the signal”. No means is mentioned which originates the gripping strength command.